Investigated the significance that the greater proportion of variance on Alumni and Awards Score have on the total score for determining the world rank of a university, when controlling for Nature and Science Score and PUB Score.
Reporting Statistics in Psychology
This document provides guidelines for reporting statistics in psychology research. It outlines how to round numbers and report means, standard deviations, p-values, effect sizes, and results from t-tests, ANOVAs, and other statistical analyses. Key recommendations include reporting exact p-values to two or three decimal places, using abbreviations like M and SD consistently, and noting any violations of statistical assumptions.
A two-way ANOVA was conducted to examine the effects of athlete type (football, basketball, soccer) and age (younger, older) on slices of pizza eaten. There were significant main effects of athlete type and an interaction between athlete type and age, but no main effect of age. Football players ate the most pizza, followed by basketball players and then soccer players.
This document provides guidelines for writing up results sections based on APA style. It discusses reporting statistical tests, including describing test statistics, significance levels, means, standard deviations, and directions of effects. Examples are provided for how to report results from t-tests, ANOVAs, post hoc tests, chi-square tests, correlations, and regressions. Tables and figures can help report complex results. The guidelines emphasize identifying analyses and their relation to hypotheses, and assuming reader knowledge of statistics.
The document discusses statistics concepts in analyzing test score data from two samples of students and females. The mean test score for the student sample was 81 with a standard deviation of 4, while the mean for the female sample was also 81 with a standard deviation of 3.8. The coefficient of variation shows that the student sample had more variation in score distribution. Other concepts discussed include skewness, kurtosis and measures of central tendency.
This document discusses strategies for designing factorial experiments with multiple factors. It explains that factorial experiments involve studying the effect of varying levels of factors on a response variable. The optimal design strategy depends on whether the circumstances are unusual or normal. For normal circumstances where there is some noise and factors influence each other, a fractional factorial or full factorial design is typically best. The document provides details on analyzing the data from factorial experiments to determine if factor effects and interactions are significant. It includes examples of calculating main effects and interactions from 2-level factorial data.
Introduction to Business Analytics Course Part 9Beamsync
Beamsync is providing "Business Analytics Training in Bangalore" with experience faculty. If you are looking for analytics courses in Bengaluru consult beamsync.
For more details visit: http://beamsync.com/business-analytics-training-bangalore/
Introduction to Business Analytics Course Part 10Beamsync
Are you looking for Business Analytics training courses in Bangalore? then consult Beamsync.
Beamsync is providing business analytics training in Bengaluru / Bangalore with experience trainers. For schedules visit: http://beamsync.com/business-analytics-training-bangalore/
This document provides information on two-way repeated measures designs, including when to use them, their structure, and how to analyze the data. A two-way repeated measures design is used to investigate the effects of two within-subjects factors on a dependent variable simultaneously. All subjects are tested at each level of both factors. This design allows comparison of mean differences between groups split on the two within-subject factors. The document describes the analysis process, including testing for main effects, interactions, and simple effects using SPSS. An example is provided to illustrate a two-way repeated measures design investigating the effects of music and environment on work performance.
Reporting Statistics in Psychology
This document provides guidelines for reporting statistics in psychology research. It outlines how to round numbers and report means, standard deviations, p-values, effect sizes, and results from t-tests, ANOVAs, and other statistical analyses. Key recommendations include reporting exact p-values to two or three decimal places, using abbreviations like M and SD consistently, and noting any violations of statistical assumptions.
A two-way ANOVA was conducted to examine the effects of athlete type (football, basketball, soccer) and age (younger, older) on slices of pizza eaten. There were significant main effects of athlete type and an interaction between athlete type and age, but no main effect of age. Football players ate the most pizza, followed by basketball players and then soccer players.
This document provides guidelines for writing up results sections based on APA style. It discusses reporting statistical tests, including describing test statistics, significance levels, means, standard deviations, and directions of effects. Examples are provided for how to report results from t-tests, ANOVAs, post hoc tests, chi-square tests, correlations, and regressions. Tables and figures can help report complex results. The guidelines emphasize identifying analyses and their relation to hypotheses, and assuming reader knowledge of statistics.
The document discusses statistics concepts in analyzing test score data from two samples of students and females. The mean test score for the student sample was 81 with a standard deviation of 4, while the mean for the female sample was also 81 with a standard deviation of 3.8. The coefficient of variation shows that the student sample had more variation in score distribution. Other concepts discussed include skewness, kurtosis and measures of central tendency.
This document discusses strategies for designing factorial experiments with multiple factors. It explains that factorial experiments involve studying the effect of varying levels of factors on a response variable. The optimal design strategy depends on whether the circumstances are unusual or normal. For normal circumstances where there is some noise and factors influence each other, a fractional factorial or full factorial design is typically best. The document provides details on analyzing the data from factorial experiments to determine if factor effects and interactions are significant. It includes examples of calculating main effects and interactions from 2-level factorial data.
Introduction to Business Analytics Course Part 9Beamsync
Beamsync is providing "Business Analytics Training in Bangalore" with experience faculty. If you are looking for analytics courses in Bengaluru consult beamsync.
For more details visit: http://beamsync.com/business-analytics-training-bangalore/
Introduction to Business Analytics Course Part 10Beamsync
Are you looking for Business Analytics training courses in Bangalore? then consult Beamsync.
Beamsync is providing business analytics training in Bengaluru / Bangalore with experience trainers. For schedules visit: http://beamsync.com/business-analytics-training-bangalore/
This document provides information on two-way repeated measures designs, including when to use them, their structure, and how to analyze the data. A two-way repeated measures design is used to investigate the effects of two within-subjects factors on a dependent variable simultaneously. All subjects are tested at each level of both factors. This design allows comparison of mean differences between groups split on the two within-subject factors. The document describes the analysis process, including testing for main effects, interactions, and simple effects using SPSS. An example is provided to illustrate a two-way repeated measures design investigating the effects of music and environment on work performance.
This presentation deals with the basics of design of experiments and discusses all the three basic statistical designs i.e. CRD, RBD and LSD. Further it explains the guidelines for developing experimental research.
Lesson 23 planning data analyses using statisticsmjlobetos
This document discusses strategies for analyzing collected data, including descriptive and inferential statistics. Descriptive statistics like measures of central tendency (mean, median, mode) and dispersion (range, standard deviation) are used to summarize and describe data. Inferential statistics like t-tests, ANOVA, and tests of correlation can analyze relationships, differences between groups, and make generalizations from samples to populations. The document provides formulas and examples of how to calculate and interpret various statistical measures.
This study investigated the effects of various educational indicators like class size, school size, gender mix, teacher-student ratios, and gender on 12th grade student performance in Oman. The researchers used data from Oman's Ministry of Education on over 90,000 students from 843 schools over 2 academic years. Multiple linear regression found that gender had the strongest effect, with male students performing better on average. Larger school sizes and class sizes negatively impacted performance. The study concludes that gender is the most influential factor on exam results and further analysis is needed to understand and reduce gaps between male and female students.
Repeated measures ANOVA is used to compare mean scores on the same individuals across multiple time points or conditions. It extends the dependent t-test to allow for more than two time points or conditions. Key assumptions include having a continuous dependent variable, at least two related groups or conditions, no outliers, normally distributed differences between groups, and sphericity. Repeated measures ANOVA separates variance into between-subjects, between-measures, and error components to test if there are differences in mean scores between related groups while accounting for correlations between measures on the same individuals.
A researcher tested the effectiveness of an herbal supplement on physical fitness using the Marine Physical Fitness Test. 25 college students took the supplement for 6 weeks and averaged a score of 38.68 on the test, compared to the average population score of 35. Using a t-test with α=.05 and df=24, the researcher found the average score of 38.68 was not significantly different than the population mean of 35 (t=2.041, p=.052). Therefore, there is not enough evidence to conclude the supplement had an effect on fitness levels, as the higher average score could be due to chance for this small sample.
This document provides guidance on how to report test results using APA style. It recommends using a "sandwich method" of writing with a plain English opening sentence, numeric results in order, and a plain English closing sentence. Descriptive statistics like means, standard deviations, and sample sizes should be reported. Test statistics such as t-values, degrees of freedom, p-values, and effect sizes if significant should also be included. An example reporting the results of a sleep deprivation study is given to illustrate these recommendations.
The document discusses parametric and non-parametric statistical tests. It defines parametric tests as those that make assumptions about the population distribution, such as assuming a normal distribution. Non-parametric tests make fewer assumptions. Specific tests covered include the chi-square test, run test, sign test, Kolmogorov-Smirnov test, Cochran Q test, and Friedman F test. Examples are provided for several of the tests.
- Propensity score matching (PSM) and weighting methods can be used to estimate treatment effects when selection into a treatment is based on observable characteristics.
- PSM involves matching treated units to untreated units with similar propensity scores, which is the predicted probability of receiving treatment based on observables. Weighting assigns weights inversely proportional to the probability of receiving the actual treatment.
- Both methods rely on the assumption that conditioning on observables eliminates selection bias, but there may still be bias from unobservables. Sensitivity analysis is used to check the robustness of results.
To Interpret the SPSS table of Independent sample T-Test, Paired sample T-Tes...Ranjani Balu
1. This document contains examples of how to interpret independent sample t-tests, paired sample t-tests, and one-way ANOVAs from SPSS output. It provides the null and alternative hypotheses, test statistics, and conclusions for each analysis.
2. An independent samples t-test found no significant difference in marks between two batches of students. A one-way ANOVA found a significant difference in marks among four batches, with Batch B having the highest average.
3. A one-way ANOVA found no significant difference in sales among four salespeople, so their averages were equal.
1. Students were randomly assigned to one of three groups and given a math test. The teacher then provided different levels of praise to each group: high praise to Group A, moderate praise to Group B, and no praise to Group C.
2. The next day, all students took another math test, and their scores were analyzed using ANOVA to determine if the different levels of praise had an effect.
3. The ANOVA analysis found a significant difference between the group means, with Group A scoring highest and Group C scoring lowest. This led to a rejection of the null hypothesis that the praise had no impact on test scores.
The document presents a case study where Lisa wants to open a beauty store and needs data to support her belief that women in her local area spend more than the national average of $59 every 3 months on fragrance products. Lisa takes a random sample of 25 women in her area and finds the sample mean is $68.10 with a standard deviation of $14.46. She conducts a one-sample t-test to test if the population mean is greater than $59. The test statistic is 3.1484 with a p-value of 0.0021, which is less than the significance level of 0.05. Therefore, there is sufficient evidence to conclude that the population mean is indeed greater than $
Test of significance (t-test, proportion test, chi-square test)Ramnath Takiar
The presentation discusses the concept of test of significance including the test of significance examples of t-test, proportion test and chi-square test.
Distinguish between Parameter and Statistic.
Calculate sample variance and sample standard deviation.
Visit the website for more services: https://cristinamontenegro92.wixsite.com/onevs
T test for two independent samples and inductionEmmanuel Buah
Recruitment and selection is important to find people who are a good fit for the organization to reduce costs from high turnover. A good recruitment system should be efficient, effective at finding suitable candidates, and fair by being non-discriminatory. Employers should do human resource planning to forecast needs and match available supply to demand to help with recruitment and development.
The document discusses sensitivity analysis of university ranking systems. It analyzes how single indicator perturbations and multi-indicator perturbations can significantly impact university rankings in the Academic Ranking of World Universities (ARWU) and World Famous University (WFU) systems. Simulation scenarios show how adding factors like highly cited researchers, Nobel prize winners, or publications can boost a university's ranking. The analysis aims to identify efficient ways for universities to increase their rankings.
- Regression analysis is used to predict the value of a dependent variable based on the value of one or more independent variables. It does not necessarily imply causation.
- Regression can be used to identify discrimination and validate food/drug products. Companies use it to understand key drivers of performance.
- Multiple linear regression models involve predicting a dependent variable based on multiple independent variables. Examples include treatment costs, salary outcomes, and market share.
- Regression coefficients can be estimated using ordinary least squares to minimize the residuals between predicted and actual dependent variable values.
This document provides an overview of quantitative analysis techniques. It begins by defining quantitative analysis and its key characteristics such as being objective and emphasizing statistical probabilities. It then covers common descriptive statistics techniques for examining central tendencies, distributions, and differences between groups. Further sections explore relationship testing, model building, data types, and the quantitative analysis process. Examples are provided throughout to illustrate quantitative methods for exploring patterns in data.
This document discusses the key concepts and assumptions of multiple linear regression analysis. It begins by defining the multiple regression model as examining the linear relationship between a dependent variable (Y) and two or more independent variables (X1, X2, etc). It then provides an example using data on pie sales, price, and advertising spending to estimate a multiple regression equation. Key outputs from the regression analysis like coefficients, R-squared, standard error, and t-statistics are introduced and interpreted.
Survey of Finance and Engineering Economics Presented byMoha.docxmattinsonjanel
Survey of Finance and Engineering Economics
Presented by
Mohammed Ali Alsendi
Nadia Mohammed Daabis
Instructor
Professor Wajeeh Elali
Time Value of Money
Time value of money refers to the concept that a dollar today is worth more than a dollar tomorrow.
Case study
NATASHA, 30 years old and has Bachelor of science degree in computer science.
Working as Tier 2 field service representative for a telephony corporation located in Seattle, Washington.
She has $75,000 that recently inherited from her aunt, and invested this money in 10 years treasury bond.
Terms of Common Inputs
Current Salary $38,000/-
She don’t expect to lose any income during the Certification or while she earning her MBA.
In both cases, she expect her salary differential will also grow at a rate of 3% per year, for as long as she keep working.
Keep using the interest rate as discount rate for the remainder of the problem
CAMPARISME SUMMARYOption 1 "Network Design"Option 2 "MBA"PositionTier 3Managerial PositionCost$5,000 $25,000 / YearPeriod1 year3 years Salery Increasment$10,000 $20,000 Payment DueEnd of 1 yearBegin of each yearRiskAbove 80% on an exam at end of courseEvening program which will take 3 years to complete
Summary
Timeline
Option 1
Option 2
t0
t1
t2
t3
$38,000
$39,140
$50,614.20
$52,132.62
$38,000 x 3%
($39,140+$10,000) x 3%
$50,614.20 x 3%
($5,000)
($25,000)
($25,000)
($25,000)
$39,140
$40,314.20
$41,523.626
$38,000 x 3%
$39,140x 3%
$39,140 x 3%
t4
$53,696.59
$63,369.33
($41,523.626+$20,000) x 3%
$52,132.62x 3%
Timeline Graph
Current Sutation 38000 39140 40314.200000000004 41523.626000000004 42769.334780000005 44052.414823400009 45373.987268102013 46735.206886145075 Certificate 38000 39140 50614.200000000004 52132.626000000004 53696.604780000009 55307.50292340001 56966.728011102015 58675.729851435077 MBA 38000 39140 40314.200000000004 62123.625999999997 63987.334779999997 65906.954823399996 67884.163468101993 69920.688372145058
Yearly Income
Treasury Bond
Amount $75,000
Period 10 years
Rate 3.52% (1st June, 2009)*
A marketable, fixed-interest government debt security with a maturity of more than 10 years. Treasury bond make interest payment annualy and the income that holders receive is only taxed the federal level.
t0
t1
t2
t10
($75,000)
Treasury Bond
$9027.19
$9027.19
$9027.19
…..
PVA(ordinary) = PMT 1 – (1+k)-n
K
$75,000 = x 1 – (1+0.0352)-10
0.0352
PMT = $9027.190
[ ]
[ ]
C ...
This presentation deals with the basics of design of experiments and discusses all the three basic statistical designs i.e. CRD, RBD and LSD. Further it explains the guidelines for developing experimental research.
Lesson 23 planning data analyses using statisticsmjlobetos
This document discusses strategies for analyzing collected data, including descriptive and inferential statistics. Descriptive statistics like measures of central tendency (mean, median, mode) and dispersion (range, standard deviation) are used to summarize and describe data. Inferential statistics like t-tests, ANOVA, and tests of correlation can analyze relationships, differences between groups, and make generalizations from samples to populations. The document provides formulas and examples of how to calculate and interpret various statistical measures.
This study investigated the effects of various educational indicators like class size, school size, gender mix, teacher-student ratios, and gender on 12th grade student performance in Oman. The researchers used data from Oman's Ministry of Education on over 90,000 students from 843 schools over 2 academic years. Multiple linear regression found that gender had the strongest effect, with male students performing better on average. Larger school sizes and class sizes negatively impacted performance. The study concludes that gender is the most influential factor on exam results and further analysis is needed to understand and reduce gaps between male and female students.
Repeated measures ANOVA is used to compare mean scores on the same individuals across multiple time points or conditions. It extends the dependent t-test to allow for more than two time points or conditions. Key assumptions include having a continuous dependent variable, at least two related groups or conditions, no outliers, normally distributed differences between groups, and sphericity. Repeated measures ANOVA separates variance into between-subjects, between-measures, and error components to test if there are differences in mean scores between related groups while accounting for correlations between measures on the same individuals.
A researcher tested the effectiveness of an herbal supplement on physical fitness using the Marine Physical Fitness Test. 25 college students took the supplement for 6 weeks and averaged a score of 38.68 on the test, compared to the average population score of 35. Using a t-test with α=.05 and df=24, the researcher found the average score of 38.68 was not significantly different than the population mean of 35 (t=2.041, p=.052). Therefore, there is not enough evidence to conclude the supplement had an effect on fitness levels, as the higher average score could be due to chance for this small sample.
This document provides guidance on how to report test results using APA style. It recommends using a "sandwich method" of writing with a plain English opening sentence, numeric results in order, and a plain English closing sentence. Descriptive statistics like means, standard deviations, and sample sizes should be reported. Test statistics such as t-values, degrees of freedom, p-values, and effect sizes if significant should also be included. An example reporting the results of a sleep deprivation study is given to illustrate these recommendations.
The document discusses parametric and non-parametric statistical tests. It defines parametric tests as those that make assumptions about the population distribution, such as assuming a normal distribution. Non-parametric tests make fewer assumptions. Specific tests covered include the chi-square test, run test, sign test, Kolmogorov-Smirnov test, Cochran Q test, and Friedman F test. Examples are provided for several of the tests.
- Propensity score matching (PSM) and weighting methods can be used to estimate treatment effects when selection into a treatment is based on observable characteristics.
- PSM involves matching treated units to untreated units with similar propensity scores, which is the predicted probability of receiving treatment based on observables. Weighting assigns weights inversely proportional to the probability of receiving the actual treatment.
- Both methods rely on the assumption that conditioning on observables eliminates selection bias, but there may still be bias from unobservables. Sensitivity analysis is used to check the robustness of results.
To Interpret the SPSS table of Independent sample T-Test, Paired sample T-Tes...Ranjani Balu
1. This document contains examples of how to interpret independent sample t-tests, paired sample t-tests, and one-way ANOVAs from SPSS output. It provides the null and alternative hypotheses, test statistics, and conclusions for each analysis.
2. An independent samples t-test found no significant difference in marks between two batches of students. A one-way ANOVA found a significant difference in marks among four batches, with Batch B having the highest average.
3. A one-way ANOVA found no significant difference in sales among four salespeople, so their averages were equal.
1. Students were randomly assigned to one of three groups and given a math test. The teacher then provided different levels of praise to each group: high praise to Group A, moderate praise to Group B, and no praise to Group C.
2. The next day, all students took another math test, and their scores were analyzed using ANOVA to determine if the different levels of praise had an effect.
3. The ANOVA analysis found a significant difference between the group means, with Group A scoring highest and Group C scoring lowest. This led to a rejection of the null hypothesis that the praise had no impact on test scores.
The document presents a case study where Lisa wants to open a beauty store and needs data to support her belief that women in her local area spend more than the national average of $59 every 3 months on fragrance products. Lisa takes a random sample of 25 women in her area and finds the sample mean is $68.10 with a standard deviation of $14.46. She conducts a one-sample t-test to test if the population mean is greater than $59. The test statistic is 3.1484 with a p-value of 0.0021, which is less than the significance level of 0.05. Therefore, there is sufficient evidence to conclude that the population mean is indeed greater than $
Test of significance (t-test, proportion test, chi-square test)Ramnath Takiar
The presentation discusses the concept of test of significance including the test of significance examples of t-test, proportion test and chi-square test.
Distinguish between Parameter and Statistic.
Calculate sample variance and sample standard deviation.
Visit the website for more services: https://cristinamontenegro92.wixsite.com/onevs
T test for two independent samples and inductionEmmanuel Buah
Recruitment and selection is important to find people who are a good fit for the organization to reduce costs from high turnover. A good recruitment system should be efficient, effective at finding suitable candidates, and fair by being non-discriminatory. Employers should do human resource planning to forecast needs and match available supply to demand to help with recruitment and development.
The document discusses sensitivity analysis of university ranking systems. It analyzes how single indicator perturbations and multi-indicator perturbations can significantly impact university rankings in the Academic Ranking of World Universities (ARWU) and World Famous University (WFU) systems. Simulation scenarios show how adding factors like highly cited researchers, Nobel prize winners, or publications can boost a university's ranking. The analysis aims to identify efficient ways for universities to increase their rankings.
- Regression analysis is used to predict the value of a dependent variable based on the value of one or more independent variables. It does not necessarily imply causation.
- Regression can be used to identify discrimination and validate food/drug products. Companies use it to understand key drivers of performance.
- Multiple linear regression models involve predicting a dependent variable based on multiple independent variables. Examples include treatment costs, salary outcomes, and market share.
- Regression coefficients can be estimated using ordinary least squares to minimize the residuals between predicted and actual dependent variable values.
This document provides an overview of quantitative analysis techniques. It begins by defining quantitative analysis and its key characteristics such as being objective and emphasizing statistical probabilities. It then covers common descriptive statistics techniques for examining central tendencies, distributions, and differences between groups. Further sections explore relationship testing, model building, data types, and the quantitative analysis process. Examples are provided throughout to illustrate quantitative methods for exploring patterns in data.
This document discusses the key concepts and assumptions of multiple linear regression analysis. It begins by defining the multiple regression model as examining the linear relationship between a dependent variable (Y) and two or more independent variables (X1, X2, etc). It then provides an example using data on pie sales, price, and advertising spending to estimate a multiple regression equation. Key outputs from the regression analysis like coefficients, R-squared, standard error, and t-statistics are introduced and interpreted.
Survey of Finance and Engineering Economics Presented byMoha.docxmattinsonjanel
Survey of Finance and Engineering Economics
Presented by
Mohammed Ali Alsendi
Nadia Mohammed Daabis
Instructor
Professor Wajeeh Elali
Time Value of Money
Time value of money refers to the concept that a dollar today is worth more than a dollar tomorrow.
Case study
NATASHA, 30 years old and has Bachelor of science degree in computer science.
Working as Tier 2 field service representative for a telephony corporation located in Seattle, Washington.
She has $75,000 that recently inherited from her aunt, and invested this money in 10 years treasury bond.
Terms of Common Inputs
Current Salary $38,000/-
She don’t expect to lose any income during the Certification or while she earning her MBA.
In both cases, she expect her salary differential will also grow at a rate of 3% per year, for as long as she keep working.
Keep using the interest rate as discount rate for the remainder of the problem
CAMPARISME SUMMARYOption 1 "Network Design"Option 2 "MBA"PositionTier 3Managerial PositionCost$5,000 $25,000 / YearPeriod1 year3 years Salery Increasment$10,000 $20,000 Payment DueEnd of 1 yearBegin of each yearRiskAbove 80% on an exam at end of courseEvening program which will take 3 years to complete
Summary
Timeline
Option 1
Option 2
t0
t1
t2
t3
$38,000
$39,140
$50,614.20
$52,132.62
$38,000 x 3%
($39,140+$10,000) x 3%
$50,614.20 x 3%
($5,000)
($25,000)
($25,000)
($25,000)
$39,140
$40,314.20
$41,523.626
$38,000 x 3%
$39,140x 3%
$39,140 x 3%
t4
$53,696.59
$63,369.33
($41,523.626+$20,000) x 3%
$52,132.62x 3%
Timeline Graph
Current Sutation 38000 39140 40314.200000000004 41523.626000000004 42769.334780000005 44052.414823400009 45373.987268102013 46735.206886145075 Certificate 38000 39140 50614.200000000004 52132.626000000004 53696.604780000009 55307.50292340001 56966.728011102015 58675.729851435077 MBA 38000 39140 40314.200000000004 62123.625999999997 63987.334779999997 65906.954823399996 67884.163468101993 69920.688372145058
Yearly Income
Treasury Bond
Amount $75,000
Period 10 years
Rate 3.52% (1st June, 2009)*
A marketable, fixed-interest government debt security with a maturity of more than 10 years. Treasury bond make interest payment annualy and the income that holders receive is only taxed the federal level.
t0
t1
t2
t10
($75,000)
Treasury Bond
$9027.19
$9027.19
$9027.19
…..
PVA(ordinary) = PMT 1 – (1+k)-n
K
$75,000 = x 1 – (1+0.0352)-10
0.0352
PMT = $9027.190
[ ]
[ ]
C ...
The document discusses a study that used the Kano model to identify barriers and drivers of participant satisfaction in computer-supported collaborative learning (CSCL) environments.
The study surveyed in-service and pre-service teachers about their satisfaction with online teacher training programs. Regression analysis showed that online communication was a key driver of satisfaction, while general computer skills were essential "must-be" attributes. The facilitator's role was also important for satisfaction.
The results indicate online communication quality and participants' computer skills are most important to improve for mentoring teachers in CSCL environments. Improving facilitator guidance and online dialogues also affects satisfaction levels.
Chapter 9
Multivariable Methods
Objectives
• Define and provide examples of dependent and
independent variables in a study of a public
health problem
• Explain the principle of statistical adjustment
to a lay audience
• Organize data for regression analysis
Objectives
• Define and provide an example of confounding
• Define and provide an example of effect
modification
• Interpret coefficients in multiple linear and
multiple logistic regression analysis
Definitions
• Confounding – the distortion of the effect of a
risk factor on an outcome
• Effect Modification – a different relationship
between the risk factor and an outcome
depending on the level of another variable
Confounding
• A confounder is related to the risk factor and
also to the outcome
• Assessing confounding
– Formal tests of hypothesis
– Clinically meaningful associations
Example 9.1.
Confounding
We wish to assess the association between obesity and
incident cardiovascular disease.
Incident
CVD
No
CVD
Total
Obese 46 254 300
Not
Obese
60 640 700
Total 106 894 1000
1.78
0.086
0.153
60/700
46/300
RR
CVD
Example 9.1.
Confounding
Is age a confounder?
Age
< 50
CVD No
CVD
Total Age
50+
CVD No
CVD
Total
Obese 10 90 100 Obese 36 164 200
Not
Obese
35 465 500 Not
Obese
25 175 200
Total 45 555 600 Total 65 335 400
1.44
0.13
0.18
RR and 1.43
0.07
0.10
RR
50 Age|CVD50Age|CVD
Example 9.2.
Effect Modification
A clinical trial is run to assess the efficacy of a new drug
to increase HDL cholesterol.
N Mean Std Dev
New drug 50 40.16 4.46
Placebo 50 39.21 3.91
H0: m1m2 versus H1:m1≠m2
Z=-1.13 is not statistically significant
Example 9.2.
Effect Modification
Is there effect modification by gender?
Women N Mean Std Dev
New drug 40 38.88 3.97
Placebo 41 39.24 4.21
Men N Mean Std Dev
New drug 10 45.25 1.89
Placebo 9 39.06 2.22
Effect Modification
34
36
38
40
42
44
46
Women Men
M
e
a
n
H
D
L
Gender
Placebo
New Drug
Cochran-Mantel-Haenszel Method
• Technique to estimate association between risk
factor and outcome accounting for
confounding
• Data are organized into stratum and
associations are estimated in each stratum and
combined
Correlation and Simple Linear Regression
Analysis
• Two continuous variables
– Y= dependent, outcome variable
– X=independent, predictor variable
Relationship between age and SBP, number of
hours of exercise and percent body fat, caffeine
consumption and blood sugar level.
Correlation and Simple Linear Regression
• Correlation – nature and strength of linear
association between variables
• Regression – equation that best describes
relationship between variables
Scatter Diagram
0
5
10
15
20
25
0 5 10 15 20 25 30 35 40 45
X
Y
Correlation Coefficient
• Population correlation r
• Sample correlation r, -1 < r < +1
• Sign indicates nature of relationship (positive
or direct, negative o.
Pandemic Stress - Effect of the COVID-19 Shelter-In-Place Situation on Job Sa...Sitie F Ajmal
Group work by Sitie Ajmal, Monida Sieng, and Scott Whiteman.
Submitted as final research project for the course BUS 235B (Business Research) Spring 2020 session at San Jose State University.
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1. World Rank of a
University
Rohit Kamat
May 13, 2016
SDS 358
2. Introduction
Objectives: Investigate the significance of greater proportion
of variance on Alumni Score and Awards Score have on the
total score for determining the world rank of a university, when
controlling for Nature and Science Score and PUB Score.
Since I was a Junior in high school, I have been curious on
what factors caused certain universities to be ranked higher
than others.
Hypotheses: Alumni and Award score does account for a
significant greater proportion of variance for determining the world
rank of a university when controlling for Nature and Science Score
and PUB Score.
3. Methods
Sample: Subjects were the top 100 World Universities from
each year from 2005 to 2015. The total score of universities
(quantitative), alumni score (quantitative), award score
(quantitative), nature and science score (quantitative), and
PUB score (quantitative) were measured. After tagging the
observation that I will use for the model, I had 1101 samples.
From using the three outs method, I had 18 outliers that were
removed.
Analysis Method: A sequential multiple regression was
performed.
4. Descriptives
Response Variable:
Mean SD Min Max
Total Score 36.38 13.56 23.5 100
Explanatory Variables:
Mean SD Min Max
NS Score
(Control Variable)
16.08 12.51 0 100
PUB Score
(Control Variable)
38.25 13.05 7.3 100
Alumni Score 9.16 14.14 0 100
Award Score 7.69 15.49 0 100
5. Results
Overall Significance of Variance Change:
Model RSS Df F-value P-value
Control Variables 24598.20
+ Variables of Interest 2892.30 2 4045.1 <.001
Regression Table:
Model 1 (Just Control Variables):
Coefficient Estimate SE t-value P-value
Intercept 5.32279 .66173 8.044 <.001
NS 0.76845 .01287 59.073 <.001
PUB 0.10881 .01541 7.062 <.001
Overall Model Fit: F(2,1080)= 3544, p<.001; Multiple R2
= 0.8678, Adjusted R2
=0.8675
6. Model 2 (With Variable of Interest)
Overall Model Fit: F(4,1078) = 1.074 * 104
, p < 0.001; Multiple R2
= 0.9845, Adjusted R2
= 0.9844
Difference of Variance between Variables of Interest and Control
Variables: 0.1166785
Coefficient Estimate SE t-value P-value
Intercept 1.244374 .238944 5.208 <.001
NS .410574 .006180 66.436 <.001
PUB .24210 .005943 40.741 <.001
Alumni .109702 .004260 25.753 <.001
Award .218962 .004365 50.166 <.001
7. 0 200 400 600 800 1000
0.0000.0050.0100.0150.020
Cook's Distance
Index
Cook'sDistance
Assumptions
Assumptions: To check for multicollinearity I used the Variation
Inflation Factor (VIF). The Variance Inflation Factor for all variables was
under 5, so there was no multicollinearity in the model. A Residual vs.
Fitted Plot was used to check and confirmed Homoscedasticity. Cook’s
Distance Plot and the use of Three Outs was used to determine outlier
removal.
8. Discussion
Interpretation: The variance change from the first model to the
second model was significant with F(2,1078)= 4045.1, p<.05. Difference of
variance between the variable of interest and the control variables is
0.1166785. The overall full model (second model) was significant with
F(4,1078)= 1 * 104
, p<.05. The individual slopes of the full model showed a
positive impact on the total score of a university ranking for NS Score
(=0.410574, t(1078)=66.436, p<.05), PUB Score (=0.242120,
t(1078)=40.741, p<.05), Alumni Score (=0.109702, t(1078)=25.743,
p<.05), Award Score (=0.218962, t(1078)=50.166, p<.05).
Limitations: The model did not take into account other
factors that impact the total score of a university such as the
HiCi Score and the PCP score.
9. Implications: Several indicators for determining the rank of a
university were based on research, papers published and
awards based on staff members and alumni from the
institution. I think factors related to the students such as
employment of graduates, quality of professors, value of
degree from the school in terms of income, graduation rate
should also determine the rank of the university.
References: The methodology and use of the Academic
Ranking of World University website.
http://www.shanghairanking.com/